A Preliminary Forecast of the Production Status of China’s Daqing Oil field from the Perspective of EROI
Bibliographic record
Abstract
Energy return on investment (EROI) and net energy are useful metrics for analyzing energy production physically rather than monetarily. However, these metrics are not widely applied in China. In this study, we forecast the Daqing oilfield’s EROI from 2013 to 2025 using existing data for crude oil and natural gas production and the basic rules of EROI. Unfortunately, our calculations indicate that the oilfield’s EROI will continuously decline from 7.3 to 4.7, and the associated net energy will continuously decline from 1.53 × 1012 MJ to 1.25 × 1012 MJ. If China’s energy intensity does not decline as planned in the next ten years, then the EROI of Daqing will be even lower than our estimates. Additionally, relating the EROI to the monetary return on investment (MROI) in a low production and high intensity scenario, Daqing’s EROI will decline to 2.9 and its MROI will decline to 1.8 by 2025. If the “law of minimum EROI” and the assumed “minimum MROI” are taken into account, then we estimate that both energy pressure and economic pressure will restrict Daqing’s production by 2025.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".